A Data Desensitization Algorithm for Privacy Protection Electric Power Industry
Zhenying Tang, Wei Zhao, Chenfei Wang, Zixing Yang, Xu Yin, Shenhao Cui · IOP Conference Series Materials Science and Engineering · 2020
Abstract In view of the data security problems faced by big data technology in the development of electric power industry, this paper proposes a power big data desensitization algorithm applied to privacy protection, that is, a binary K-clustering algorithm (BKC-LDA) based on K-anonymity and L diversity. First, in order to reduce the computational complexity, a classification attribute is determined to classify the data table initially, and the equivalent class number K and the sensitive attribute value category L are limited according to the number of the original ancestor in the source data table. Then, considering the influence of the change of the internal range of the attribute value on the clustering, the equation for calculating the distance between the original ancestors with weight is established, using the idea of greedy and binary K clustering to classify the data table initially sets are clustered and generalized. In addition, this big data desensitization method determines the security level policies of different permissions by adjusting the size of K and L. Our algorithm can adapt to the desensitization of power big data with different attributes in different scenarios. It can not only fully mine the value of data, but also effectively protect the privacy of users.